A solution for predicting the Customer Lifetime Value of different market segments
Resumo
The estimation of Customer Lifetime Value (CLV) has gained importance in understanding the relationship between companies and their customers. However, its implementation often encounters difficulties, ranging from solutions being highly specific to the context for which they are developed to the attributes used violating customer privacy. This work aims to build a generic solution for CLV prediction, using only attributes derived from the date and value of customer transactions with the company. The developed model was evaluated on five different datasets, demonstrating its applicability, in addition to being compared with five reference solutions from the literature. The results showed that it was possible to achieve a reduction of up to 34% in CLV calculation error in the scenarios.
Palavras-chave:
LTV, CLV, Machine Learning
Referências
Abidar, L., ZAIDOUNI, D., ASRI, I. E., and ENNOUAARY, A. Predicting customer segment changes to enhance customer retention: A case study for online retail using machine learning. International Journal of Advanced Computer Science and Applications 14 (7), 2023.
Bauer, J. and Jannach, D. Improved customer lifetime value prediction with sequence-to-sequence learning and feature-based models. ACM Trans. Knowl. Discov. Data 15 (5), may, 2021.
Calabourdin, A. V. and Aksenov, K. A. Streaming bayesian modeling for predicting fat-tailed customer lifetime value, 2023.
Comlan, M. and Adiba, E. Customer lifetime value in streaming: a markov chain approach. In 2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA). IEEE, pp. 1–6, 2024.
Ekinci, Y., Uray, N., and Ülengin, F. A customer lifetime value model for the banking industry: a guide to marketing actions. European Journal of Marketing 48 (3/4): 761–784, 2014.
Fader, P. S. and Hardie, B. G. The gamma-gamma model of monetary value. February vol. 2, pp. 1–9, 2013.
Fader, P. S., Hardie, B. G., and Lee, K. L. “counting your customers” the easy way: An alternative to the pareto/nbd model. Marketing science 24 (2): 275–284, 2005.
Haenlein, M., Kaplan, A. M., and Beeser, A. J. A model to determine customer lifetime value in a retail banking context. European Management Journal 25 (3): 221–234, 2007.
Hiziroglu, A. and Sengul, S. Investigating two customer lifetime value models from segmentation perspective. Procedia-Social and Behavioral Sciences vol. 62, pp. 766–774, 2012.
Kailash, H., Kanwar, K., Sonia, S., and Kant, R. Machine learning algorithms for predicting customers’ lifetime value: A systematic evaluation. In 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). pp. 538–541, 2023.
Khajvand, M., Zolfaghar, K., Ashoori, S., and Alizadeh, S. Estimating customer lifetime value based on rfm analysis of customer purchase behavior: Case study. Procedia computer science vol. 3, pp. 57–63, 2011.
Kruger, E. Top market strategy: Applying the 80/20 rule, 2011.
Kumar, A., Singh, K. U., Kumar, G., Choudhury, T., and Kotecha, K. Customer lifetime value prediction: Using machine learning to forecast clv and enhance customer relationship management. In 2023 7th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). pp. 1–7, 2023.
POPA, A.-L., SASU, D. V., and TARCZA, T. M. Investigating The Importance Of Customer Lifetime Value In Modern Marketing - A Literature Review. Annals of Faculty of Economics 30 (2): 410–416, December, 2021.
Qi, J., Qu, Q.-X., Zhou, Y.-P., and li, L. The impact of users’ characteristics on customer lifetime value raising: evidence from mobile data service in china. Information Technology and Management vol. 16, pp. 273–290, 12, 2015.
Ramos, J. and Silva, F. Customer lifetime value prediction: A machine learning approach. In Anais do XX Encontro Nacional de Inteligência Artificial e Computacional. SBC, Porto Alegre, RS, Brasil, pp. 486–500, 2023.
Reinartz and Kumar. On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing. Journal of Marketing vol. 64, pp. 17–35, 10, 2000.
Schmittlein, D. C., Morrison, D. G., and Colombo, R. Counting your customers: Who are they and what will they do next? Management Science 33 (1): 1–24, 1987.
Sun, Y., Liu, H., and Gao, Y. Research on customer lifetime value based on machine learning algorithms and customer relationship management analysis model. Heliyon 9 (2), 2023.
Temor Qismat, Y. F. Comparison of classical RFM models and Machine learning models in CLV prediction. M.S. thesis, Norwegian Business School BI Open, Oslo, 2020.
Vanderveld, A., Pandey, A., Han, A., and Parekh, R. An engagement-based customer lifetime value system for e-commerce. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16. Association for Computing Machinery, New York, NY, USA, pp. 293–302, 2016.
Bauer, J. and Jannach, D. Improved customer lifetime value prediction with sequence-to-sequence learning and feature-based models. ACM Trans. Knowl. Discov. Data 15 (5), may, 2021.
Calabourdin, A. V. and Aksenov, K. A. Streaming bayesian modeling for predicting fat-tailed customer lifetime value, 2023.
Comlan, M. and Adiba, E. Customer lifetime value in streaming: a markov chain approach. In 2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA). IEEE, pp. 1–6, 2024.
Ekinci, Y., Uray, N., and Ülengin, F. A customer lifetime value model for the banking industry: a guide to marketing actions. European Journal of Marketing 48 (3/4): 761–784, 2014.
Fader, P. S. and Hardie, B. G. The gamma-gamma model of monetary value. February vol. 2, pp. 1–9, 2013.
Fader, P. S., Hardie, B. G., and Lee, K. L. “counting your customers” the easy way: An alternative to the pareto/nbd model. Marketing science 24 (2): 275–284, 2005.
Haenlein, M., Kaplan, A. M., and Beeser, A. J. A model to determine customer lifetime value in a retail banking context. European Management Journal 25 (3): 221–234, 2007.
Hiziroglu, A. and Sengul, S. Investigating two customer lifetime value models from segmentation perspective. Procedia-Social and Behavioral Sciences vol. 62, pp. 766–774, 2012.
Kailash, H., Kanwar, K., Sonia, S., and Kant, R. Machine learning algorithms for predicting customers’ lifetime value: A systematic evaluation. In 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). pp. 538–541, 2023.
Khajvand, M., Zolfaghar, K., Ashoori, S., and Alizadeh, S. Estimating customer lifetime value based on rfm analysis of customer purchase behavior: Case study. Procedia computer science vol. 3, pp. 57–63, 2011.
Kruger, E. Top market strategy: Applying the 80/20 rule, 2011.
Kumar, A., Singh, K. U., Kumar, G., Choudhury, T., and Kotecha, K. Customer lifetime value prediction: Using machine learning to forecast clv and enhance customer relationship management. In 2023 7th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). pp. 1–7, 2023.
POPA, A.-L., SASU, D. V., and TARCZA, T. M. Investigating The Importance Of Customer Lifetime Value In Modern Marketing - A Literature Review. Annals of Faculty of Economics 30 (2): 410–416, December, 2021.
Qi, J., Qu, Q.-X., Zhou, Y.-P., and li, L. The impact of users’ characteristics on customer lifetime value raising: evidence from mobile data service in china. Information Technology and Management vol. 16, pp. 273–290, 12, 2015.
Ramos, J. and Silva, F. Customer lifetime value prediction: A machine learning approach. In Anais do XX Encontro Nacional de Inteligência Artificial e Computacional. SBC, Porto Alegre, RS, Brasil, pp. 486–500, 2023.
Reinartz and Kumar. On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing. Journal of Marketing vol. 64, pp. 17–35, 10, 2000.
Schmittlein, D. C., Morrison, D. G., and Colombo, R. Counting your customers: Who are they and what will they do next? Management Science 33 (1): 1–24, 1987.
Sun, Y., Liu, H., and Gao, Y. Research on customer lifetime value based on machine learning algorithms and customer relationship management analysis model. Heliyon 9 (2), 2023.
Temor Qismat, Y. F. Comparison of classical RFM models and Machine learning models in CLV prediction. M.S. thesis, Norwegian Business School BI Open, Oslo, 2020.
Vanderveld, A., Pandey, A., Han, A., and Parekh, R. An engagement-based customer lifetime value system for e-commerce. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16. Association for Computing Machinery, New York, NY, USA, pp. 293–302, 2016.
Publicado
17/11/2024
Como Citar
RAMOS, J. M. A. M.; SILVA, F. A..
A solution for predicting the Customer Lifetime Value of different market segments. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 12. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 81-88.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2024.244681.
